Lupus: influence of number of patients - simulation results
Results for celltypes = T4_naive
Milan Malfait
April 07, 2023
library(here)
library(tidyverse)
theme_set(theme_light())
library(scuttle)
library(iCOBRA)
library(kableExtra)
library(SCandwichCompanion)Setup
## Directory setup
here_root <- "benchmarks/lupus-n_patients"
here::i_am(file.path(here_root, "analysis/lupus-n_patients-sim-results.Rmd"))
#> here() starts at /Users/jg/Desktop/PhD/DD_project/DD_git
res_dir <- here::here(here_root, "results")
fig_dir <- here::here(here_root, "figures")- Using
n_patients: 10 20 30 - Using
methods: edgeR_QP, edgeR_NB, qbGLM_offset_squeeze, bGLM, qbGLM, qbGLM_offset - Using
celltype: T4_naive - Using
prop_DE: 0.05
Load results
# TODO: also add all-patients results for comparison?
res_files <- map(n_patients, ~ get_sim_res_files(
dataset = "lupus-n_patients",
methods = methods,
prop_DE = prop_DE,
celltype = celltype,
n_patients = .x
)) %>%
set_names(paste0("n_patients_", n_patients))
res_list <- map_depth(res_files, 2, readRDS)
str(res_list, max.level = 3)
#> List of 3
#> $ n_patients_10:List of 6
#> ..$ edgeR_QP :List of 5
#> .. ..$ replicate_1:List of 2
#> .. ..$ replicate_2:List of 2
#> .. ..$ replicate_3:List of 2
#> .. ..$ replicate_4:List of 2
#> .. ..$ replicate_5:List of 2
#> ..$ edgeR_NB :List of 5
#> .. ..$ replicate_1:List of 2
#> .. ..$ replicate_2:List of 2
#> .. ..$ replicate_3:List of 2
#> .. ..$ replicate_4:List of 2
#> .. ..$ replicate_5:List of 2
#> ..$ qbGLM_offset_squeeze:List of 5
#> .. ..$ replicate_1:List of 2
#> .. ..$ replicate_2:List of 2
#> .. ..$ replicate_3:List of 2
#> .. ..$ replicate_4:List of 2
#> .. ..$ replicate_5:List of 2
#> ..$ bGLM :List of 5
#> .. ..$ replicate_1:List of 2
#> .. ..$ replicate_2:List of 2
#> .. ..$ replicate_3:List of 2
#> .. ..$ replicate_4:List of 2
#> .. ..$ replicate_5:List of 2
#> ..$ qbGLM :List of 5
#> .. ..$ replicate_1:List of 2
#> .. ..$ replicate_2:List of 2
#> .. ..$ replicate_3:List of 2
#> .. ..$ replicate_4:List of 2
#> .. ..$ replicate_5:List of 2
#> ..$ qbGLM_offset :List of 5
#> .. ..$ replicate_1:List of 2
#> .. ..$ replicate_2:List of 2
#> .. ..$ replicate_3:List of 2
#> .. ..$ replicate_4:List of 2
#> .. ..$ replicate_5:List of 2
#> $ n_patients_20:List of 6
#> ..$ edgeR_QP :List of 5
#> .. ..$ replicate_1:List of 2
#> .. ..$ replicate_2:List of 2
#> .. ..$ replicate_3:List of 2
#> .. ..$ replicate_4:List of 2
#> .. ..$ replicate_5:List of 2
#> ..$ edgeR_NB :List of 5
#> .. ..$ replicate_1:List of 2
#> .. ..$ replicate_2:List of 2
#> .. ..$ replicate_3:List of 2
#> .. ..$ replicate_4:List of 2
#> .. ..$ replicate_5:List of 2
#> ..$ qbGLM_offset_squeeze:List of 5
#> .. ..$ replicate_1:List of 2
#> .. ..$ replicate_2:List of 2
#> .. ..$ replicate_3:List of 2
#> .. ..$ replicate_4:List of 2
#> .. ..$ replicate_5:List of 2
#> ..$ bGLM :List of 5
#> .. ..$ replicate_1:List of 2
#> .. ..$ replicate_2:List of 2
#> .. ..$ replicate_3:List of 2
#> .. ..$ replicate_4:List of 2
#> .. ..$ replicate_5:List of 2
#> ..$ qbGLM :List of 5
#> .. ..$ replicate_1:List of 2
#> .. ..$ replicate_2:List of 2
#> .. ..$ replicate_3:List of 2
#> .. ..$ replicate_4:List of 2
#> .. ..$ replicate_5:List of 2
#> ..$ qbGLM_offset :List of 5
#> .. ..$ replicate_1:List of 2
#> .. ..$ replicate_2:List of 2
#> .. ..$ replicate_3:List of 2
#> .. ..$ replicate_4:List of 2
#> .. ..$ replicate_5:List of 2
#> $ n_patients_30:List of 6
#> ..$ edgeR_QP :List of 5
#> .. ..$ replicate_1:List of 2
#> .. ..$ replicate_2:List of 2
#> .. ..$ replicate_3:List of 2
#> .. ..$ replicate_4:List of 2
#> .. ..$ replicate_5:List of 2
#> ..$ edgeR_NB :List of 5
#> .. ..$ replicate_1:List of 2
#> .. ..$ replicate_2:List of 2
#> .. ..$ replicate_3:List of 2
#> .. ..$ replicate_4:List of 2
#> .. ..$ replicate_5:List of 2
#> ..$ qbGLM_offset_squeeze:List of 5
#> .. ..$ replicate_1:List of 2
#> .. ..$ replicate_2:List of 2
#> .. ..$ replicate_3:List of 2
#> .. ..$ replicate_4:List of 2
#> .. ..$ replicate_5:List of 2
#> ..$ bGLM :List of 5
#> .. ..$ replicate_1:List of 2
#> .. ..$ replicate_2:List of 2
#> .. ..$ replicate_3:List of 2
#> .. ..$ replicate_4:List of 2
#> .. ..$ replicate_5:List of 2
#> ..$ qbGLM :List of 5
#> .. ..$ replicate_1:List of 2
#> .. ..$ replicate_2:List of 2
#> .. ..$ replicate_3:List of 2
#> .. ..$ replicate_4:List of 2
#> .. ..$ replicate_5:List of 2
#> ..$ qbGLM_offset :List of 5
#> .. ..$ replicate_1:List of 2
#> .. ..$ replicate_2:List of 2
#> .. ..$ replicate_3:List of 2
#> .. ..$ replicate_4:List of 2
#> .. ..$ replicate_5:List of 2Load SCE objects
data_files <- map(n_patients, ~ get_SCE_files(
dataset = "lupus-n_patients", which = "sim_replicates",
celltype = celltype, n_patients = .x, prop_DE = prop_DE
)) %>%
set_names(paste0("n_patients_", n_patients))
sce_objects <- map(data_files, readRDS)
str(sce_objects, max.level = 3)
#> List of 3
#> $ n_patients_10:List of 5
#> ..$ replicate_1:Formal class 'SingleCellExperiment' [package "SingleCellExperiment"] with 9 slots
#> ..$ replicate_2:Formal class 'SingleCellExperiment' [package "SingleCellExperiment"] with 9 slots
#> ..$ replicate_3:Formal class 'SingleCellExperiment' [package "SingleCellExperiment"] with 9 slots
#> ..$ replicate_4:Formal class 'SingleCellExperiment' [package "SingleCellExperiment"] with 9 slots
#> ..$ replicate_5:Formal class 'SingleCellExperiment' [package "SingleCellExperiment"] with 9 slots
#> $ n_patients_20:List of 5
#> ..$ replicate_1:Formal class 'SingleCellExperiment' [package "SingleCellExperiment"] with 9 slots
#> ..$ replicate_2:Formal class 'SingleCellExperiment' [package "SingleCellExperiment"] with 9 slots
#> ..$ replicate_3:Formal class 'SingleCellExperiment' [package "SingleCellExperiment"] with 9 slots
#> ..$ replicate_4:Formal class 'SingleCellExperiment' [package "SingleCellExperiment"] with 9 slots
#> ..$ replicate_5:Formal class 'SingleCellExperiment' [package "SingleCellExperiment"] with 9 slots
#> $ n_patients_30:List of 5
#> ..$ replicate_1:Formal class 'SingleCellExperiment' [package "SingleCellExperiment"] with 9 slots
#> ..$ replicate_2:Formal class 'SingleCellExperiment' [package "SingleCellExperiment"] with 9 slots
#> ..$ replicate_3:Formal class 'SingleCellExperiment' [package "SingleCellExperiment"] with 9 slots
#> ..$ replicate_4:Formal class 'SingleCellExperiment' [package "SingleCellExperiment"] with 9 slots
#> ..$ replicate_5:Formal class 'SingleCellExperiment' [package "SingleCellExperiment"] with 9 slotsData overview
- The results were generated from 5 mock replicates
- Each replicate was generated by randomly splitting the subjects in two mock groups
- No sub-sampling of cells per patient was performed for this data
- DE was introduced by randomly swapping genes in one of the mock groups
- The number of patients was randomly sub-sampled stratified by mock group
Note that for n_patients 10 and 20, the
selected patients vary randomly between the replicates, which is why the
nrows and ncols are not identical. For the
n_patients 30, however, no random sub-sampling was done
because I could just use all patients from 2 out of the original 3
batches from the Control samples. That’s why the ncols are
identical for those subsets.
The mock group assignment of each subject also varies across replicates.
map_dfr(sce_objects,
~ map_dfr(.x, function(x) c(nrows = nrow(x), ncols = ncol(x)),
.id = "replicate"
),
.id = "n_patients"
)Subjects are divided across mock groups as follows:
n_patients = 10
replicate_1
| A | B | |
|---|---|---|
| IGTB1793_IGTB1793 | 1 | 0 |
| IGTB1815_IGTB1815 | 1 | 0 |
| IGTB1828_IGTB1828 | 1 | 0 |
| IGTB1895_IGTB1895 | 0 | 1 |
| IGTB1901_IGTB1901 | 0 | 1 |
| IGTB1952_IGTB1952 | 0 | 1 |
| IGTB1966_IGTB1966 | 0 | 1 |
| IGTB1982_IGTB1982 | 0 | 1 |
| IGTB2007_IGTB2007 | 1 | 0 |
| IGTB2065_IGTB2065 | 1 | 0 |
replicate_2
| A | B | |
|---|---|---|
| IGTB1762_IGTB1762 | 1 | 0 |
| IGTB1789_IGTB1789 | 0 | 1 |
| IGTB1793_IGTB1793 | 0 | 1 |
| IGTB1815_IGTB1815 | 1 | 0 |
| IGTB1828_IGTB1828 | 0 | 1 |
| IGTB1840_IGTB1840 | 1 | 0 |
| IGTB1901_IGTB1901 | 1 | 0 |
| IGTB1996_IGTB1996 | 0 | 1 |
| IGTB2007_IGTB2007 | 0 | 1 |
| IGTB2065_IGTB2065 | 1 | 0 |
replicate_3
| A | B | |
|---|---|---|
| IGTB1762_IGTB1762 | 0 | 1 |
| IGTB1793_IGTB1793 | 1 | 0 |
| IGTB1815_IGTB1815 | 1 | 0 |
| IGTB1828_IGTB1828 | 0 | 1 |
| IGTB1871_IGTB1871 | 1 | 0 |
| IGTB1895_IGTB1895 | 0 | 1 |
| IGTB1952_IGTB1952 | 1 | 0 |
| IGTB1966_IGTB1966 | 0 | 1 |
| IGTB1996_IGTB1996 | 1 | 0 |
| IGTB2007_IGTB2007 | 0 | 1 |
replicate_4
| A | B | |
|---|---|---|
| IGTB1762_IGTB1762 | 0 | 1 |
| IGTB1793_IGTB1793 | 0 | 1 |
| IGTB1828_IGTB1828 | 0 | 1 |
| IGTB1840_IGTB1840 | 1 | 0 |
| IGTB1895_IGTB1895 | 1 | 0 |
| IGTB1966_IGTB1966 | 1 | 0 |
| IGTB1982_IGTB1982 | 0 | 1 |
| IGTB1996_IGTB1996 | 1 | 0 |
| IGTB2007_IGTB2007 | 1 | 0 |
| IGTB2065_IGTB2065 | 0 | 1 |
replicate_5
| A | B | |
|---|---|---|
| IGTB1789_IGTB1789 | 0 | 1 |
| IGTB1815_IGTB1815 | 1 | 0 |
| IGTB1828_IGTB1828 | 1 | 0 |
| IGTB1840_IGTB1840 | 1 | 0 |
| IGTB1895_IGTB1895 | 1 | 0 |
| IGTB1952_IGTB1952 | 0 | 1 |
| IGTB1966_IGTB1966 | 0 | 1 |
| IGTB1996_IGTB1996 | 1 | 0 |
| IGTB2007_IGTB2007 | 0 | 1 |
| IGTB2065_IGTB2065 | 0 | 1 |
n_patients = 20
replicate_1
| A | B | |
|---|---|---|
| IGTB143_IGTB143 | 1 | 0 |
| IGTB469_IGTB469 | 0 | 1 |
| IGTB498_IGTB498 | 0 | 1 |
| IGTB508_IGTB508 | 1 | 0 |
| IGTB514_IGTB514 | 0 | 1 |
| IGTB645_IGTB645 | 0 | 1 |
| IGTB670_IGTB670 | 1 | 0 |
| IGTB826_IGTB826 | 1 | 0 |
| IGTB1539_IGTB1539 | 1 | 0 |
| IGTB1762_IGTB1762 | 1 | 0 |
| IGTB1828_IGTB1828 | 1 | 0 |
| IGTB1840_IGTB1840 | 1 | 0 |
| IGTB1871_IGTB1871 | 0 | 1 |
| IGTB1901_IGTB1901 | 0 | 1 |
| IGTB1952_IGTB1952 | 0 | 1 |
| IGTB1966_IGTB1966 | 0 | 1 |
| IGTB1982_IGTB1982 | 0 | 1 |
| IGTB1996_IGTB1996 | 0 | 1 |
| IGTB2007_IGTB2007 | 1 | 0 |
| IGTB2065_IGTB2065 | 1 | 0 |
replicate_2
| A | B | |
|---|---|---|
| IGTB141_IGTB141 | 1 | 0 |
| IGTB195_IGTB195 | 1 | 0 |
| IGTB469_IGTB469 | 1 | 0 |
| IGTB514_IGTB514 | 0 | 1 |
| IGTB645_IGTB645 | 1 | 0 |
| IGTB670_IGTB670 | 0 | 1 |
| IGTB1372_IGTB1372 | 0 | 1 |
| IGTB1506_IGTB1506 | 0 | 1 |
| IGTB1575_IGTB1575 | 0 | 1 |
| IGTB1789_IGTB1789 | 0 | 1 |
| IGTB1815_IGTB1815 | 1 | 0 |
| IGTB1828_IGTB1828 | 0 | 1 |
| IGTB1871_IGTB1871 | 0 | 1 |
| IGTB1895_IGTB1895 | 1 | 0 |
| IGTB1901_IGTB1901 | 1 | 0 |
| IGTB1906_IGTB1906 | 0 | 1 |
| IGTB1952_IGTB1952 | 0 | 1 |
| IGTB1966_IGTB1966 | 1 | 0 |
| IGTB1982_IGTB1982 | 1 | 0 |
| IGTB2065_IGTB2065 | 1 | 0 |
replicate_3
| A | B | |
|---|---|---|
| IGTB141_IGTB141 | 1 | 0 |
| IGTB143_IGTB143 | 0 | 1 |
| IGTB195_IGTB195 | 1 | 0 |
| IGTB469_IGTB469 | 0 | 1 |
| IGTB498_IGTB498 | 1 | 0 |
| IGTB514_IGTB514 | 1 | 0 |
| IGTB826_IGTB826 | 0 | 1 |
| IGTB1372_IGTB1372 | 1 | 0 |
| IGTB1575_IGTB1575 | 0 | 1 |
| IGTB1762_IGTB1762 | 0 | 1 |
| IGTB1789_IGTB1789 | 1 | 0 |
| IGTB1815_IGTB1815 | 1 | 0 |
| IGTB1828_IGTB1828 | 0 | 1 |
| IGTB1840_IGTB1840 | 1 | 0 |
| IGTB1901_IGTB1901 | 0 | 1 |
| IGTB1906_IGTB1906 | 0 | 1 |
| IGTB1952_IGTB1952 | 1 | 0 |
| IGTB1966_IGTB1966 | 0 | 1 |
| IGTB1982_IGTB1982 | 0 | 1 |
| IGTB1996_IGTB1996 | 1 | 0 |
replicate_4
| A | B | |
|---|---|---|
| IGTB141_IGTB141 | 1 | 0 |
| IGTB143_IGTB143 | 0 | 1 |
| IGTB508_IGTB508 | 1 | 0 |
| IGTB670_IGTB670 | 0 | 1 |
| IGTB1506_IGTB1506 | 0 | 1 |
| IGTB1539_IGTB1539 | 0 | 1 |
| IGTB1762_IGTB1762 | 0 | 1 |
| IGTB1789_IGTB1789 | 0 | 1 |
| IGTB1793_IGTB1793 | 0 | 1 |
| IGTB1815_IGTB1815 | 1 | 0 |
| IGTB1840_IGTB1840 | 1 | 0 |
| IGTB1871_IGTB1871 | 1 | 0 |
| IGTB1895_IGTB1895 | 1 | 0 |
| IGTB1901_IGTB1901 | 0 | 1 |
| IGTB1906_IGTB1906 | 1 | 0 |
| IGTB1952_IGTB1952 | 0 | 1 |
| IGTB1966_IGTB1966 | 1 | 0 |
| IGTB1996_IGTB1996 | 1 | 0 |
| IGTB2007_IGTB2007 | 1 | 0 |
| IGTB2065_IGTB2065 | 0 | 1 |
replicate_5
| A | B | |
|---|---|---|
| IGTB141_IGTB141 | 0 | 1 |
| IGTB143_IGTB143 | 0 | 1 |
| IGTB508_IGTB508 | 1 | 0 |
| IGTB514_IGTB514 | 1 | 0 |
| IGTB645_IGTB645 | 1 | 0 |
| IGTB826_IGTB826 | 1 | 0 |
| IGTB1372_IGTB1372 | 0 | 1 |
| IGTB1575_IGTB1575 | 1 | 0 |
| IGTB1789_IGTB1789 | 0 | 1 |
| IGTB1793_IGTB1793 | 1 | 0 |
| IGTB1828_IGTB1828 | 1 | 0 |
| IGTB1840_IGTB1840 | 1 | 0 |
| IGTB1871_IGTB1871 | 1 | 0 |
| IGTB1901_IGTB1901 | 0 | 1 |
| IGTB1952_IGTB1952 | 0 | 1 |
| IGTB1966_IGTB1966 | 0 | 1 |
| IGTB1982_IGTB1982 | 0 | 1 |
| IGTB1996_IGTB1996 | 1 | 0 |
| IGTB2007_IGTB2007 | 0 | 1 |
| IGTB2065_IGTB2065 | 0 | 1 |
n_patients = 30
replicate_1
| A | B | |
|---|---|---|
| IGTB141_IGTB141 | 0 | 1 |
| IGTB143_IGTB143 | 1 | 0 |
| IGTB195_IGTB195 | 1 | 0 |
| IGTB469_IGTB469 | 0 | 1 |
| IGTB498_IGTB498 | 0 | 1 |
| IGTB508_IGTB508 | 1 | 0 |
| IGTB514_IGTB514 | 0 | 1 |
| IGTB645_IGTB645 | 0 | 1 |
| IGTB670_IGTB670 | 1 | 0 |
| IGTB826_IGTB826 | 1 | 0 |
| IGTB1372_IGTB1372 | 0 | 1 |
| IGTB1506_IGTB1506 | 0 | 1 |
| IGTB1539_IGTB1539 | 1 | 0 |
| IGTB1575_IGTB1575 | 1 | 0 |
| IGTB1762_IGTB1762 | 1 | 0 |
| IGTB1789_IGTB1789 | 0 | 1 |
| IGTB1793_IGTB1793 | 1 | 0 |
| IGTB1815_IGTB1815 | 1 | 0 |
| IGTB1828_IGTB1828 | 1 | 0 |
| IGTB1840_IGTB1840 | 1 | 0 |
| IGTB1871_IGTB1871 | 0 | 1 |
| IGTB1895_IGTB1895 | 0 | 1 |
| IGTB1901_IGTB1901 | 0 | 1 |
| IGTB1906_IGTB1906 | 1 | 0 |
| IGTB1952_IGTB1952 | 0 | 1 |
| IGTB1966_IGTB1966 | 0 | 1 |
| IGTB1982_IGTB1982 | 0 | 1 |
| IGTB1996_IGTB1996 | 0 | 1 |
| IGTB2007_IGTB2007 | 1 | 0 |
| IGTB2065_IGTB2065 | 1 | 0 |
replicate_2
| A | B | |
|---|---|---|
| IGTB141_IGTB141 | 1 | 0 |
| IGTB143_IGTB143 | 1 | 0 |
| IGTB195_IGTB195 | 1 | 0 |
| IGTB469_IGTB469 | 1 | 0 |
| IGTB498_IGTB498 | 1 | 0 |
| IGTB508_IGTB508 | 1 | 0 |
| IGTB514_IGTB514 | 0 | 1 |
| IGTB645_IGTB645 | 1 | 0 |
| IGTB670_IGTB670 | 0 | 1 |
| IGTB826_IGTB826 | 0 | 1 |
| IGTB1372_IGTB1372 | 0 | 1 |
| IGTB1506_IGTB1506 | 0 | 1 |
| IGTB1539_IGTB1539 | 0 | 1 |
| IGTB1575_IGTB1575 | 0 | 1 |
| IGTB1762_IGTB1762 | 1 | 0 |
| IGTB1789_IGTB1789 | 0 | 1 |
| IGTB1793_IGTB1793 | 0 | 1 |
| IGTB1815_IGTB1815 | 1 | 0 |
| IGTB1828_IGTB1828 | 0 | 1 |
| IGTB1840_IGTB1840 | 1 | 0 |
| IGTB1871_IGTB1871 | 0 | 1 |
| IGTB1895_IGTB1895 | 1 | 0 |
| IGTB1901_IGTB1901 | 1 | 0 |
| IGTB1906_IGTB1906 | 0 | 1 |
| IGTB1952_IGTB1952 | 0 | 1 |
| IGTB1966_IGTB1966 | 1 | 0 |
| IGTB1982_IGTB1982 | 1 | 0 |
| IGTB1996_IGTB1996 | 0 | 1 |
| IGTB2007_IGTB2007 | 0 | 1 |
| IGTB2065_IGTB2065 | 1 | 0 |
replicate_3
| A | B | |
|---|---|---|
| IGTB141_IGTB141 | 1 | 0 |
| IGTB143_IGTB143 | 0 | 1 |
| IGTB195_IGTB195 | 1 | 0 |
| IGTB469_IGTB469 | 0 | 1 |
| IGTB498_IGTB498 | 1 | 0 |
| IGTB508_IGTB508 | 1 | 0 |
| IGTB514_IGTB514 | 1 | 0 |
| IGTB645_IGTB645 | 1 | 0 |
| IGTB670_IGTB670 | 0 | 1 |
| IGTB826_IGTB826 | 0 | 1 |
| IGTB1372_IGTB1372 | 1 | 0 |
| IGTB1506_IGTB1506 | 1 | 0 |
| IGTB1539_IGTB1539 | 0 | 1 |
| IGTB1575_IGTB1575 | 0 | 1 |
| IGTB1762_IGTB1762 | 0 | 1 |
| IGTB1789_IGTB1789 | 1 | 0 |
| IGTB1793_IGTB1793 | 1 | 0 |
| IGTB1815_IGTB1815 | 1 | 0 |
| IGTB1828_IGTB1828 | 0 | 1 |
| IGTB1840_IGTB1840 | 1 | 0 |
| IGTB1871_IGTB1871 | 1 | 0 |
| IGTB1895_IGTB1895 | 0 | 1 |
| IGTB1901_IGTB1901 | 0 | 1 |
| IGTB1906_IGTB1906 | 0 | 1 |
| IGTB1952_IGTB1952 | 1 | 0 |
| IGTB1966_IGTB1966 | 0 | 1 |
| IGTB1982_IGTB1982 | 0 | 1 |
| IGTB1996_IGTB1996 | 1 | 0 |
| IGTB2007_IGTB2007 | 0 | 1 |
| IGTB2065_IGTB2065 | 1 | 0 |
replicate_4
| A | B | |
|---|---|---|
| IGTB141_IGTB141 | 1 | 0 |
| IGTB143_IGTB143 | 0 | 1 |
| IGTB195_IGTB195 | 0 | 1 |
| IGTB469_IGTB469 | 1 | 0 |
| IGTB498_IGTB498 | 0 | 1 |
| IGTB508_IGTB508 | 1 | 0 |
| IGTB514_IGTB514 | 1 | 0 |
| IGTB645_IGTB645 | 1 | 0 |
| IGTB670_IGTB670 | 0 | 1 |
| IGTB826_IGTB826 | 0 | 1 |
| IGTB1372_IGTB1372 | 0 | 1 |
| IGTB1506_IGTB1506 | 0 | 1 |
| IGTB1539_IGTB1539 | 0 | 1 |
| IGTB1575_IGTB1575 | 1 | 0 |
| IGTB1762_IGTB1762 | 0 | 1 |
| IGTB1789_IGTB1789 | 0 | 1 |
| IGTB1793_IGTB1793 | 0 | 1 |
| IGTB1815_IGTB1815 | 1 | 0 |
| IGTB1828_IGTB1828 | 0 | 1 |
| IGTB1840_IGTB1840 | 1 | 0 |
| IGTB1871_IGTB1871 | 1 | 0 |
| IGTB1895_IGTB1895 | 1 | 0 |
| IGTB1901_IGTB1901 | 0 | 1 |
| IGTB1906_IGTB1906 | 1 | 0 |
| IGTB1952_IGTB1952 | 0 | 1 |
| IGTB1966_IGTB1966 | 1 | 0 |
| IGTB1982_IGTB1982 | 0 | 1 |
| IGTB1996_IGTB1996 | 1 | 0 |
| IGTB2007_IGTB2007 | 1 | 0 |
| IGTB2065_IGTB2065 | 0 | 1 |
replicate_5
| A | B | |
|---|---|---|
| IGTB141_IGTB141 | 0 | 1 |
| IGTB143_IGTB143 | 0 | 1 |
| IGTB195_IGTB195 | 1 | 0 |
| IGTB469_IGTB469 | 0 | 1 |
| IGTB498_IGTB498 | 1 | 0 |
| IGTB508_IGTB508 | 1 | 0 |
| IGTB514_IGTB514 | 1 | 0 |
| IGTB645_IGTB645 | 1 | 0 |
| IGTB670_IGTB670 | 0 | 1 |
| IGTB826_IGTB826 | 1 | 0 |
| IGTB1372_IGTB1372 | 0 | 1 |
| IGTB1506_IGTB1506 | 1 | 0 |
| IGTB1539_IGTB1539 | 0 | 1 |
| IGTB1575_IGTB1575 | 1 | 0 |
| IGTB1762_IGTB1762 | 0 | 1 |
| IGTB1789_IGTB1789 | 0 | 1 |
| IGTB1793_IGTB1793 | 1 | 0 |
| IGTB1815_IGTB1815 | 1 | 0 |
| IGTB1828_IGTB1828 | 1 | 0 |
| IGTB1840_IGTB1840 | 1 | 0 |
| IGTB1871_IGTB1871 | 1 | 0 |
| IGTB1895_IGTB1895 | 1 | 0 |
| IGTB1901_IGTB1901 | 0 | 1 |
| IGTB1906_IGTB1906 | 0 | 1 |
| IGTB1952_IGTB1952 | 0 | 1 |
| IGTB1966_IGTB1966 | 0 | 1 |
| IGTB1982_IGTB1982 | 0 | 1 |
| IGTB1996_IGTB1996 | 1 | 0 |
| IGTB2007_IGTB2007 | 0 | 1 |
| IGTB2065_IGTB2065 | 0 | 1 |
The number of DE and non-DE genes per replicate:
map(sce_objects, ~ map_dfr(.x, ~ table(rowData(.x)$is_DE), .id = "replicate"))
#> $n_patients_10
#> # A tibble: 5 × 3
#> replicate `FALSE` `TRUE`
#> <chr> <table> <table>
#> 1 replicate_1 3792 201
#> 2 replicate_2 3799 201
#> 3 replicate_3 3801 201
#> 4 replicate_4 3786 201
#> 5 replicate_5 3794 201
#>
#> $n_patients_20
#> # A tibble: 5 × 3
#> replicate `FALSE` `TRUE`
#> <chr> <table> <table>
#> 1 replicate_1 3659 199
#> 2 replicate_2 3609 199
#> 3 replicate_3 3639 199
#> 4 replicate_4 3759 201
#> 5 replicate_5 3696 201
#>
#> $n_patients_30
#> # A tibble: 5 × 3
#> replicate `FALSE` `TRUE`
#> <chr> <table> <table>
#> 1 replicate_1 3637 198
#> 2 replicate_2 3642 200
#> 3 replicate_3 3638 200
#> 4 replicate_4 3634 201
#> 5 replicate_5 3634 200t-SNE plots
tSNE_plots <- map(sce_objects, function(x) {
p <- imap(x, function(sce, name) {
scater::plotTSNE(sce, colour_by = "mock_group") +
ggtitle(name)
})
patchwork::wrap_plots(p, ncol = 3, guides = "collect")
})n_patients = 10
n_patients = 20
n_patients = 30
Extract results of interest
Runtimes
## Get runtimes for each celltype
runtimes <- map_dfr(res_list,
~ map_dfr(.x, get_runtimes, depth = 1, .id = "method"),
.id = "n_patients"
) %>%
mutate(n_patients = as.numeric(sub("n_patients_", "", n_patients)))P-values
res_tables <- map_depth(res_list, 2, get_aggregated_rep_tables, depth = 1)
res_tables <- map(res_tables, ~ combine_tables(.x, .id = "method"))Visualize results
Run times
ggplot(runtimes, aes(n_patients, time, col = method)) +
geom_jitter(width = 0.5, alpha = 0.6) +
geom_smooth(se = FALSE, method = "lm", formula = y ~ x) +
labs(x = "Number of patients", y = "Time (seconds)", color = NULL) +
scale_x_continuous(breaks = unique(runtimes$n_patients), minor_breaks = NULL)
P-value distributions for non-DE genes
non_de_res <- map2(sce_objects, res_tables, function(sce_list, res) {
by_rep <- split(res, res$replicate)
out <- map2(sce_list, by_rep, function(sce, tbl) {
## Select only non-DE genes
non_de <- rownames(sce)[!rowData(sce)$is_DE]
tbl[tbl$gene %in% non_de, ]
})
bind_rows(out, .id = "replicate")
})non_de_pval_figs <- map(non_de_res, ~ pval_hist(.x))n_patients_10
n_patients_20
n_patients_30
Performance evaluation with iCOBRA
Prepare Data
P-values for missing genes are set to 1.
cobra_data <- map2(res_tables, sce_objects, function(res_table, sce_list) {
## Split up results per replicate
res_per_replicate <- split(res_table, res_table$replicate)
map2(res_per_replicate, sce_list, prepare_COBRAData, replace_missing = TRUE)
})
cobra_perf <- map_depth(cobra_data, 2, calculate_performance, binary_truth = "status")
cobra_objects <- map_depth(cobra_perf, 2, prepare_data_for_plot)FDR-TPR curves
fdr_tpr_plots <- map(cobra_objects, function(cobra_list) {
p <- imap(cobra_list, ~ plot_fdrtprcurve(.x, title = .y))
patchwork::wrap_plots(p, ncol = 3, guides = "collect")
})n_patients_10
n_patients_20
n_patients_30
FDR-TPR plots averaged across replicate
## Working points
fdr_tpr_points <- map(cobra_objects, combine_fdrtpr_tables) %>%
bind_rows(.id = "n_patients") %>%
mutate(n_patients = as.numeric(sub("n_patients_", "", n_patients)))
fdr_tpr_points_averaged <- fdr_tpr_points %>%
group_by(thr, method, n_patients) %>%
summarize(across(c(FDR, TPR), mean), .groups = "keep")plot_fdrtpr_points(fdr_tpr_points_averaged) +
facet_wrap(vars(n_patients), labeller = "label_both") +
xlim(c(0, 0.45)) + ylim(c(0, 1))
#> Warning: Removed 3 row(s) containing missing values (geom_path).
#> Warning: Removed 3 rows containing missing values (geom_point).
FDR control at threshold 0.05
use_thr <- 0.05
fdr_data <- filter(fdr_tpr_points, thr == use_thr)
plot_fdr_control(fdr_data, use_thr = use_thr) +
ylim(c(0, 1)) +
facet_wrap(vars(n_patients), labeller = "label_both")
Session info
Session info
#> [1] "2023-04-07 12:18:42 CEST"
#> Local: main /Users/jg/Desktop/PhD/DD_project/DD_git
#> Remote: main @ origin (git@github.com:jgilis/DD_benchmark.git)
#> Head: [1540c5e] 2023-04-06: update description
#> ─ Session info ─────────────────────────────────────────────────────────────────────
#> setting value
#> version R version 4.1.3 (2022-03-10)
#> os macOS Big Sur/Monterey 10.16
#> system x86_64, darwin17.0
#> ui X11
#> language (EN)
#> collate C
#> ctype UTF-8
#> tz Europe/Brussels
#> date 2023-04-07
#> pandoc 2.17.1.1 @ /Users/jg/opt/anaconda3/bin/ (via rmarkdown)
#>
#> ─ Packages ─────────────────────────────────────────────────────────────────────────
#> ! package * version date (UTC) lib source
#> annotate 1.72.0 2021-10-26 [2] Bioconductor
#> AnnotationDbi 1.56.2 2021-11-09 [2] Bioconductor
#> P argparse * 2.1.2 2021-10-21 [?] CRAN (R 4.1.0)
#> assertthat 0.2.1 2019-03-21 [2] CRAN (R 4.1.0)
#> P backports 1.3.0 2021-10-27 [?] CRAN (R 4.1.0)
#> beachmat 2.10.0 2021-10-26 [2] Bioconductor
#> beeswarm 0.4.0 2021-06-01 [2] CRAN (R 4.1.0)
#> Biobase * 2.54.0 2021-10-26 [2] Bioconductor
#> BiocGenerics * 0.40.0 2021-10-26 [2] Bioconductor
#> P BiocManager 1.30.16 2021-06-15 [?] CRAN (R 4.1.0)
#> BiocNeighbors 1.12.0 2021-10-26 [2] Bioconductor
#> P BiocParallel 1.28.1 2021-11-18 [?] Bioconductor
#> BiocSingular 1.10.0 2021-10-26 [2] Bioconductor
#> Biostrings 2.62.0 2021-10-26 [2] Bioconductor
#> bit 4.0.4 2020-08-04 [2] CRAN (R 4.1.0)
#> bit64 4.0.5 2020-08-30 [2] CRAN (R 4.1.0)
#> P bitops 1.0-7 2021-04-24 [?] CRAN (R 4.1.0)
#> blme 1.0-5 2021-01-05 [2] CRAN (R 4.1.0)
#> P blob 1.2.2 2021-07-23 [?] CRAN (R 4.1.0)
#> bluster 1.4.0 2021-10-26 [2] Bioconductor
#> boot 1.3-28 2021-05-03 [2] CRAN (R 4.1.3)
#> P broom 0.7.10 2021-10-31 [?] CRAN (R 4.1.0)
#> P bslib 0.3.1 2021-10-06 [?] CRAN (R 4.1.0)
#> cachem 1.0.6 2021-08-19 [2] CRAN (R 4.1.0)
#> P caTools 1.18.2 2021-03-28 [?] CRAN (R 4.1.0)
#> cellranger 1.1.0 2016-07-27 [2] CRAN (R 4.1.0)
#> P circlize 0.4.13 2021-06-09 [?] CRAN (R 4.1.0)
#> P cli 3.6.1 2023-03-23 [?] CRAN (R 4.1.2)
#> clue 0.3-60 2021-10-11 [2] CRAN (R 4.1.0)
#> P cluster 2.1.2 2021-04-17 [?] CRAN (R 4.1.0)
#> codetools 0.2-18 2020-11-04 [2] CRAN (R 4.1.3)
#> P colorspace 2.0-2 2021-06-24 [?] CRAN (R 4.1.0)
#> ComplexHeatmap 2.10.0 2021-10-26 [2] Bioconductor
#> cowplot 1.1.1 2020-12-30 [2] CRAN (R 4.1.0)
#> P crayon 1.4.2 2021-10-29 [?] CRAN (R 4.1.0)
#> data.table 1.14.2 2021-09-27 [2] CRAN (R 4.1.0)
#> P DBI 1.1.1 2021-01-15 [?] CRAN (R 4.1.0)
#> dbplyr 2.1.1 2021-04-06 [2] CRAN (R 4.1.0)
#> DelayedArray 0.20.0 2021-10-26 [2] Bioconductor
#> DelayedMatrixStats 1.16.0 2021-10-26 [2] Bioconductor
#> DESeq2 1.34.0 2021-10-26 [2] Bioconductor
#> P digest 0.6.28 2021-09-23 [?] CRAN (R 4.1.0)
#> P doParallel 1.0.16 2020-10-16 [?] CRAN (R 4.1.0)
#> P dplyr * 1.0.7 2021-06-18 [?] CRAN (R 4.1.0)
#> dqrng 0.3.0 2021-05-01 [2] CRAN (R 4.1.0)
#> P DT 0.20 2021-11-15 [?] CRAN (R 4.1.0)
#> edgeR 3.36.0 2021-10-26 [2] Bioconductor
#> ellipsis 0.3.2 2021-04-29 [2] CRAN (R 4.1.0)
#> P evaluate 0.14 2019-05-28 [?] CRAN (R 4.1.0)
#> P fansi 1.0.2 2022-01-14 [?] CRAN (R 4.1.2)
#> P farver 2.1.0 2021-02-28 [?] CRAN (R 4.1.0)
#> fastmap 1.1.0 2021-01-25 [2] CRAN (R 4.1.0)
#> P findpython 1.0.7 2021-01-27 [?] CRAN (R 4.1.0)
#> forcats * 0.5.1 2021-01-27 [2] CRAN (R 4.1.0)
#> P foreach 1.5.1 2020-10-15 [?] CRAN (R 4.1.0)
#> P fs 1.6.1 2023-02-06 [?] CRAN (R 4.1.2)
#> P future 1.23.0 2021-10-31 [?] CRAN (R 4.1.0)
#> P future.apply 1.8.1 2021-08-10 [?] CRAN (R 4.1.0)
#> genefilter 1.76.0 2021-10-26 [2] Bioconductor
#> geneplotter 1.72.0 2021-10-26 [2] Bioconductor
#> P generics 0.1.1 2021-10-25 [?] CRAN (R 4.1.0)
#> P GenomeInfoDb * 1.30.0 2021-10-26 [?] Bioconductor
#> GenomeInfoDbData 1.2.7 2021-12-18 [2] Bioconductor
#> GenomicRanges * 1.46.1 2021-11-18 [2] Bioconductor
#> GetoptLong 1.0.5 2020-12-15 [2] CRAN (R 4.1.0)
#> ggbeeswarm 0.6.0 2017-08-07 [2] CRAN (R 4.1.0)
#> P ggplot2 * 3.3.5.9000 2023-03-30 [?] Github (tidyverse/ggplot2@199eb90)
#> ggrepel 0.9.1 2021-01-15 [2] CRAN (R 4.1.0)
#> P git2r 0.29.0 2021-11-22 [?] CRAN (R 4.1.0)
#> P glmmTMB 1.1.2.3 2021-09-20 [?] CRAN (R 4.1.0)
#> GlobalOptions 0.1.2 2020-06-10 [2] CRAN (R 4.1.0)
#> P globals 0.14.0 2020-11-22 [?] CRAN (R 4.1.0)
#> P glue 1.6.0 2021-12-17 [?] CRAN (R 4.1.0)
#> P gplots 3.1.1 2020-11-28 [?] CRAN (R 4.1.0)
#> gridExtra 2.3 2017-09-09 [2] CRAN (R 4.1.0)
#> P gtable 0.3.0 2019-03-25 [?] CRAN (R 4.1.0)
#> P gtools 3.9.2 2021-06-06 [?] CRAN (R 4.1.0)
#> P haven 2.4.3 2021-08-04 [?] CRAN (R 4.1.0)
#> here * 1.0.1 2020-12-13 [2] CRAN (R 4.1.0)
#> highr 0.9 2021-04-16 [2] CRAN (R 4.1.0)
#> hms 1.1.1 2021-09-26 [2] CRAN (R 4.1.0)
#> P htmltools 0.5.2 2021-08-25 [?] CRAN (R 4.1.0)
#> htmlwidgets 1.5.4 2021-09-08 [2] CRAN (R 4.1.0)
#> P httpuv 1.6.3 2021-09-09 [?] CRAN (R 4.1.0)
#> P httr 1.4.2 2020-07-20 [?] CRAN (R 4.1.0)
#> P iCOBRA * 1.22.1 2021-11-03 [?] Bioconductor
#> P igraph 1.2.8 2021-11-07 [?] CRAN (R 4.1.0)
#> IRanges * 2.28.0 2021-10-26 [2] Bioconductor
#> P irlba 2.3.3 2019-02-05 [?] CRAN (R 4.1.0)
#> P iterators 1.0.13 2020-10-15 [?] CRAN (R 4.1.0)
#> jquerylib 0.1.4 2021-04-26 [2] CRAN (R 4.1.0)
#> P jsonlite 1.7.2 2020-12-09 [?] CRAN (R 4.1.0)
#> P kableExtra * 1.3.4 2021-02-20 [?] CRAN (R 4.1.2)
#> KEGGREST 1.34.0 2021-10-26 [2] Bioconductor
#> KernSmooth 2.23-20 2021-05-03 [2] CRAN (R 4.1.3)
#> P knitr 1.36 2021-09-29 [?] CRAN (R 4.1.0)
#> labeling 0.4.2 2020-10-20 [2] CRAN (R 4.1.0)
#> later 1.3.0 2021-08-18 [2] CRAN (R 4.1.0)
#> lattice 0.20-45 2021-09-22 [2] CRAN (R 4.1.3)
#> P lifecycle 1.0.1 2021-09-24 [?] CRAN (R 4.1.0)
#> P limma 3.50.0 2021-10-26 [?] Bioconductor
#> listenv 0.8.0 2019-12-05 [2] CRAN (R 4.1.0)
#> P lme4 1.1-27.1 2021-06-22 [?] CRAN (R 4.1.0)
#> lmerTest 3.1-3 2020-10-23 [2] CRAN (R 4.1.0)
#> P locfit 1.5-9.4 2020-03-25 [?] CRAN (R 4.1.0)
#> lubridate 1.8.0 2021-10-07 [2] CRAN (R 4.1.0)
#> P magrittr 2.0.1 2020-11-17 [?] CRAN (R 4.1.0)
#> P MASS 7.3-54 2021-05-03 [?] CRAN (R 4.1.0)
#> P Matrix 1.3-4 2021-06-01 [?] CRAN (R 4.1.0)
#> MatrixGenerics * 1.6.0 2021-10-26 [2] Bioconductor
#> P matrixStats * 0.61.0 2021-09-17 [?] CRAN (R 4.1.0)
#> P memoise 2.0.1 2021-11-26 [?] CRAN (R 4.1.0)
#> metapod 1.2.0 2021-10-26 [2] Bioconductor
#> P mgcv 1.8-38 2021-10-06 [?] CRAN (R 4.1.0)
#> mime 0.12 2021-09-28 [2] CRAN (R 4.1.0)
#> minqa 1.2.4 2014-10-09 [2] CRAN (R 4.1.0)
#> modelr 0.1.8 2020-05-19 [2] CRAN (R 4.1.0)
#> munsell 0.5.0 2018-06-12 [2] CRAN (R 4.1.0)
#> P muscat 1.8.0 2021-10-26 [?] Bioconductor
#> P nlme 3.1-153 2021-09-07 [?] CRAN (R 4.1.0)
#> P nloptr 1.2.2.3 2021-11-02 [?] CRAN (R 4.1.0)
#> numDeriv 2016.8-1.1 2019-06-06 [2] CRAN (R 4.1.0)
#> P parallelly 1.29.0 2021-11-21 [?] CRAN (R 4.1.0)
#> P patchwork 1.1.1 2020-12-17 [?] CRAN (R 4.1.0)
#> pbkrtest 0.5.1 2021-03-09 [2] CRAN (R 4.1.0)
#> P pillar 1.6.4 2021-10-18 [?] CRAN (R 4.1.0)
#> pkgconfig 2.0.3 2019-09-22 [2] CRAN (R 4.1.0)
#> P plyr 1.8.6 2020-03-03 [?] CRAN (R 4.1.0)
#> png 0.1-7 2013-12-03 [2] CRAN (R 4.1.0)
#> prettyunits 1.1.1 2020-01-24 [2] CRAN (R 4.1.0)
#> progress 1.2.2 2019-05-16 [2] CRAN (R 4.1.0)
#> promises 1.2.0.1 2021-02-11 [2] CRAN (R 4.1.0)
#> P purrr * 0.3.4 2020-04-17 [?] CRAN (R 4.1.0)
#> R6 2.5.1 2021-08-19 [2] CRAN (R 4.1.0)
#> P RColorBrewer 1.1-2 2014-12-07 [?] CRAN (R 4.1.0)
#> P Rcpp 1.0.7 2021-07-07 [?] CRAN (R 4.1.0)
#> P RCurl 1.98-1.5 2021-09-17 [?] CRAN (R 4.1.0)
#> P readr * 2.1.0 2021-11-11 [?] CRAN (R 4.1.0)
#> P readxl 1.3.1 2019-03-13 [?] CRAN (R 4.1.0)
#> renv 0.15.5 2022-05-26 [1] CRAN (R 4.1.2)
#> reprex 2.0.1 2021-08-05 [2] CRAN (R 4.1.0)
#> reshape2 1.4.4 2020-04-09 [2] CRAN (R 4.1.0)
#> P rjson 0.2.20 2018-06-08 [?] CRAN (R 4.1.0)
#> P rlang 1.1.0 2023-03-14 [?] CRAN (R 4.1.2)
#> P rmarkdown 2.16 2022-08-24 [?] CRAN (R 4.1.3)
#> P ROCR 1.0-11 2020-05-02 [?] CRAN (R 4.1.0)
#> VP rprojroot 2.0.3 2020-11-15 [2] CRAN (R 4.1.0) (on disk 2.0.2)
#> P RSQLite 2.2.8 2021-08-21 [?] CRAN (R 4.1.0)
#> rstudioapi 0.13 2020-11-12 [2] CRAN (R 4.1.0)
#> rsvd 1.0.5 2021-04-16 [2] CRAN (R 4.1.0)
#> rvest 1.0.2 2021-10-16 [2] CRAN (R 4.1.0)
#> P S4Vectors * 0.32.2 2021-11-07 [?] Bioconductor
#> P sass 0.4.0 2021-05-12 [?] CRAN (R 4.1.0)
#> ScaledMatrix 1.2.0 2021-10-26 [2] Bioconductor
#> P scales 1.1.1 2020-05-11 [?] CRAN (R 4.1.0)
#> SCandwichCompanion * 0.1.0 2023-04-04 [1] local
#> scater 1.22.0 2021-10-26 [2] Bioconductor
#> scran 1.22.1 2021-11-14 [2] Bioconductor
#> P sctransform 0.3.2 2020-12-16 [?] CRAN (R 4.1.0)
#> scuttle * 1.4.0 2021-10-26 [2] Bioconductor
#> P sessioninfo 1.2.2 2021-12-06 [?] CRAN (R 4.1.0)
#> shape 1.4.6 2021-05-19 [2] CRAN (R 4.1.0)
#> P shiny 1.7.1 2021-10-02 [?] CRAN (R 4.1.0)
#> P shinyBS 0.61 2015-03-31 [?] CRAN (R 4.1.0)
#> shinydashboard 0.7.2 2021-09-30 [2] CRAN (R 4.1.0)
#> SingleCellExperiment * 1.16.0 2021-10-26 [2] Bioconductor
#> sparseMatrixStats 1.6.0 2021-10-26 [2] Bioconductor
#> P statmod 1.4.36 2021-05-10 [?] CRAN (R 4.1.0)
#> P stringi 1.7.5 2021-10-04 [?] CRAN (R 4.1.0)
#> P stringr * 1.4.0 2019-02-10 [?] CRAN (R 4.1.0)
#> SummarizedExperiment * 1.24.0 2021-10-26 [2] Bioconductor
#> P survival 3.2-13 2021-08-24 [?] CRAN (R 4.1.0)
#> P svglite 2.0.0 2021-02-20 [?] CRAN (R 4.1.0)
#> P systemfonts 1.0.3 2021-10-13 [?] CRAN (R 4.1.2)
#> P tibble * 3.1.6 2021-11-07 [?] CRAN (R 4.1.0)
#> P tidyr * 1.1.4 2021-09-27 [?] CRAN (R 4.1.0)
#> P tidyselect 1.1.1 2021-04-30 [?] CRAN (R 4.1.0)
#> tidyverse * 1.3.1 2021-04-15 [2] CRAN (R 4.1.0)
#> P TMB 1.7.22 2021-09-28 [?] CRAN (R 4.1.0)
#> P tzdb 0.2.0 2021-10-27 [?] CRAN (R 4.1.0)
#> UpSetR 1.4.0 2019-05-22 [2] CRAN (R 4.1.0)
#> P utf8 1.2.2 2021-07-24 [?] CRAN (R 4.1.0)
#> P variancePartition 1.24.0 2021-10-26 [?] Bioconductor
#> P vctrs 0.3.8 2021-04-29 [?] CRAN (R 4.1.0)
#> vipor 0.4.5 2017-03-22 [2] CRAN (R 4.1.0)
#> viridis 0.6.2 2021-10-13 [2] CRAN (R 4.1.0)
#> P viridisLite 0.4.0 2021-04-13 [?] CRAN (R 4.1.0)
#> P webshot 0.5.2 2019-11-22 [?] CRAN (R 4.1.0)
#> P withr 2.4.2 2021-04-18 [?] CRAN (R 4.1.0)
#> P xfun 0.33 2022-09-12 [?] CRAN (R 4.1.3)
#> P XML 3.99-0.8 2021-09-17 [?] CRAN (R 4.1.0)
#> P xml2 1.3.2 2020-04-23 [?] CRAN (R 4.1.0)
#> xtable 1.8-4 2019-04-21 [2] CRAN (R 4.1.0)
#> XVector 0.34.0 2021-10-26 [2] Bioconductor
#> P yaml 2.2.1 2020-02-01 [?] CRAN (R 4.1.0)
#> zlibbioc 1.40.0 2021-10-26 [2] Bioconductor
#>
#> [1] /Users/jg/Desktop/PhD/DD_project/DD_git/renv/library/R-4.1/x86_64-apple-darwin17.0
#> [2] /Library/Frameworks/R.framework/Versions/4.1/Resources/library
#>
#> V ── Loaded and on-disk version mismatch.
#> P ── Loaded and on-disk path mismatch.
#>
#> ────────────────────────────────────────────────────────────────────────────────────